Previous research has studied medical professionals' perception of artificial intelligence (AI). However, there has been a limited understanding of how healthcare consumers perceive and use AI-powered technologies such as mobile health apps. We collected 40 popular mobile health apps that claim to have adopted AI, to study how AI is explained in these apps' descriptions, and how users react to it through app reviews. We found that four AI features (Recommendation, Conversational Agent, Recognition, and Prediction) are frequently used across seven health domains, including Fitness, Mental Health, Meditation and Sleep, Nutrition and Diet, etc. Our results show that (1) users have unique expectations toward each AI features, such as including feedback for recommendations, humanlike experience for conversational agents, and accuracy for recognition and prediction; (2) when AI is not adequately described, users make their own attempts to understand AI and to find out how (well) it works.Hospital-acquired pressure ulcer injury (PUI) is a primary nursing quality metric, reflecting the caliber of nursing care within a hospital. Prior studies have used the Braden scale and structured data from the electronic health records to detect/predict PUI while the informative unstructured clinical notes have not been used. https://www.selleckchem.com/products/mi-503.html We propose automated PUI detection using a novel negation-detection algorithm applied to unstructured clinical notes. Our detection framework is on-demand, requiring minimal cost. In application to the MIMIC-III dataset, the text features produced using our algorithm resulted in improved PUI detection when evaluated using logistic regression, random forests, and neural networks compared to text features without negation detection. Exploratory analysis reveals substantial overlap between key classifier features and leading clinical attributes of PUI, adding interpretability to our solution. Our method could also considerably reduce nurses' evaluations by automatic detection of most cases, leaving only the most uncertain cases for nursing assessment.We apply deep learning-based language models to the task of patient cohort retrieval (CR) with the aim to assess their efficacy. The task ofCR requires the extraction of relevant documents from the electronic health records (EHRs) on the basis of a given query. Given the recent advancements in the field of document retrieval, we map the task of CR to a document retrieval task and apply various deep neural models implemented for the general domain tasks. In this paper, we propose a framework for retrieving patient cohorts using neural language models without the need of explicit feature engineering and domain expertise. We find that a majority of our models outperform the BM25 baseline method on various evaluation metrics.This study developed and evaluated a JSON-LD 1.1 approach to automate the Resource Description Framework (RDF) serialization and deserialization of Fast Healthcare Interoperability Resources (FHIR) data, in preparation for updating the FHIR RDF standard. We first demonstrated that this JSON-LD 1.1 approach can produce the same output as the current FHIR RDF standard. We then used it to test, document and validate several proposed changes to the FHIR RDF specification, to address usability issues that were uncovered during trial use. This JSON-LD 1.1 approach was found to be effective and more declarative than the existing custom-code-based approach, in converting FHIR data from JSON to RDF and vice versa. This approach should enable future FHIR RDF servers to be implemented and maintained more easily.Pneumonia is the most frequent cause of infectious disease-related deaths in children worldwide. Clinical decision support (CDS) applications can guide appropriate treatment, but the system must first recognize the appropriate diagnosis. To enable CDS for pediatric pneumonia, we developed an algorithm integrating natural language processing (NLP) and random forest classifiers to identify potential pediatric pneumonia from radiology reports. We deployed the algorithm in the EHR of a large children's hospital using real-time NLP. We describe the development and deployment of the algorithm, and evaluate our approach using 9-months of data gathered while the system was in use. Our model, trained on individual radiology reports, had an AUC of 0.954. The intervention, evaluated on patient encounters that could include multiple radiology reports, achieved a sensitivity, specificity, and positive predictive value of0.899, 0.949, and 0.781, respectively.Risky health behaviors such as poor diet, physical inactivity are the main contributors to the development of diabetes, one of the major causes of death and disability in the United States. Online health communities provide new avenues for individuals to efficiently manage their health conditions and adopt a positive lifestyle. So far, analysis of health-related online social exchanges has focused solely on communication content and structure of social ties, ignoring implicit user intentions underlying communication exchanges. In this paper, we propose an analytical framework to characterize communication intent, content, and social ties in online peer interactions. We integrate models from socio-behavioral sciences and linguistics with network analytics and apply it to understand Diabetes Self-Management. Results indicate the informational needs of users expressed in forms of speech acts can vary across different user engagement and disease management profiles. Implications for the design of interventions for better self-management of diabetes are discussed.Medication reconciliation (MR) aims at preventing medication errors at care transitions. It is a complex, time-consuming, cognitively demanding pharmacological task. We have developed a decision support system, EzMedRec, to assist retroactive MR at hospital admission. EzMedRec compares the best possible medication history (BPMH), i.e., all medications taken by the patient before hospitalization, to the list of admission medication orders (AMO). The process includes (i) the decomposition of BPMH and AMO drugs into their active ingredients (AIs), (ii) the detection of medication discontinuations and additions, and (iii) the identification of modified medication orders. The ATC classification is used to semantically enrich MR by comparing discontinued AIs and added AIs and suggesting a potential intentional drug substitution serving the same therapeutic objective. EzMedRec has been evaluated on a sample of 52 actual MRs involving 822 medication order lines, 406 in BPMHs, and 416 in AMOs with a global accuracy of 98,3%.